We consulted numerous publications and websites while developing the Responsible Agentic AI playbook.
Urban Institute resources
This playbook and other toolkits on responsible agentic AI are available in our GitHub repository.
The Urban Institute’s work on and with AI is collected at https://www.urban.org/research-and-evidence/artificial-intelligence. Recent Urban resources are listed below.
Blog articles about building responsible AI
How We Built an AI Evaluation Framework with Experts in the Loop
Data@Urban, July 9, 2026
What It Takes to Make Research and Policy Knowledge AI Ready
Data@Urban, July 2, 2026
Introducing AI@Urban: Practical, Evidence-Based Learning on AI in Public Policy
Data@Urban, May 12, 2026
Blog articles demonstrating the use of AI in research and policy work
AI Is Becoming a Go-To for Data Questions. How Reliable Are the Answers?
Urban Wire, May 12, 2026
How and Why AI Could Pay a Dividend to the American People
Urban Wire, March 23, 2026
Urban Wire, March 19, 2026
External resources
2025 Responsible AI Transparency Report, Microsoft
“Agentic AI,” IBM, last updated February 21, 2025
“AI Research – Explainability,” National Institute of Standards and Technology, last updated March 27, 2026
“AI Risk Management Framework,” National Institute of Standards and Technology, accessed July 17, 2026
Artificial Intelligence Risk Management Framework, National Institute of Standards and Technology, January 2023
Assessing Risks and Impacts of AI, National Institute of Standards and Technology, November 2025
ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems), MITRE, accessed July 17, 2026
Blueprint for an AI Bill of Rights, The White House, October 2022 HTML, PDF
“Design and Operationalize Agent Evaluation,” Microsoft Learn agents hub, last updated May 20, 2026
The EU Artificial Intelligence Act, particularly articles 6 (“Classification Rules for High-Risk AI Systems”) and 14 (“Human Oversight”); available via the European Commission’s AI Act Explorer
“Evidence-Based Interventions Under the ESSA,” California Department of Education, last updated January 29, 2024
“Explaining Decisions Made with AI,” Information Commissioner’s Office (UK), accessed July 20, 2026
Fairness and Machine Learning: Limitations and Opportunities, by Solon Barocas, Moritz Hardt, and Arvind Narayanan, December 2023, particularly chapter 8 ("A Broader View of Discrimination")
Four Principles of Explainable Artificial Intelligence, National Institute of Standards and Technology, September 2021
“LLM Prompt Injection Prevention Cheat Sheet,” Open Worldwide Application Security Project, accessed July 20, 2026
The Enterprise Guide to AI Governance, IBM Institute for Business Value, October 2024
“The Measure and Mismeasure of Fairness,” version 3, by Sam Corbett-Davies, Johann D. Gaebler, Hamed Nilforoshan, Ravi Shroff, and Sharad Goel, August 14, 2023
MIT’s AI Risk Initiative
Model AI Governance Framework for Agentic AI, version 1.5 (updated June 5, 2026), Infocomm Media Development Authority (Singapore)
“Overview of Responsible AI Practices for Azure OpenAI Models,” Microsoft Learn, last updated February 27, 2026
“Responsible AI” by Anka Reuel, chapter 3 of Artificial Intelligence Index Report 2025
“Responsible Use of AI for Social Impact,” NationSwell, accessed July 17, 2026
“Selecting Evidence-Based Practices for Tiers 1, 2, and 3: Navigating Clearinghouses and Databases,” US Department of Education, last updated January 14, 2025
Understanding Agentic AI: ITI’s Policy Guide, Information Technology Industry Council (ITI), November 2025:
“What Is Agentic AI?” Google Cloud, accessed July 20, 2026
“What Is Responsible AI?” Microsoft Learn, last updated September 9, 2025
Next section: Acknowledgments